Shortcuts

Source code for pytorch_lightning.utilities.rank_zero

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Utilities that can be used for calling functions on a particular rank."""
import logging
import os
import warnings
from functools import partial, wraps
from platform import python_version
from typing import Any, Callable, Optional, Union

log = logging.getLogger(__name__)


[docs]def rank_zero_only(fn: Callable) -> Callable: """Function that can be used as a decorator to enable a function/method being called only on global rank 0.""" @wraps(fn) def wrapped_fn(*args: Any, **kwargs: Any) -> Optional[Any]: if rank_zero_only.rank == 0: return fn(*args, **kwargs) return None return wrapped_fn
# TODO: this should be part of the cluster environment def _get_rank() -> int: # SLURM_PROCID can be set even if SLURM is not managing the multiprocessing, # therefore LOCAL_RANK needs to be checked first rank_keys = ("RANK", "LOCAL_RANK", "SLURM_PROCID", "JSM_NAMESPACE_RANK") for key in rank_keys: rank = os.environ.get(key) if rank is not None: return int(rank) return 0 # add the attribute to the function but don't overwrite in case Trainer has already set it rank_zero_only.rank = getattr(rank_zero_only, "rank", _get_rank()) def _info(*args: Any, stacklevel: int = 2, **kwargs: Any) -> None: if python_version() >= "3.8.0": kwargs["stacklevel"] = stacklevel log.info(*args, **kwargs) def _debug(*args: Any, stacklevel: int = 2, **kwargs: Any) -> None: if python_version() >= "3.8.0": kwargs["stacklevel"] = stacklevel log.debug(*args, **kwargs)
[docs]@rank_zero_only def rank_zero_debug(*args: Any, stacklevel: int = 4, **kwargs: Any) -> None: """Function used to log debug-level messages only on global rank 0.""" _debug(*args, stacklevel=stacklevel, **kwargs)
[docs]@rank_zero_only def rank_zero_info(*args: Any, stacklevel: int = 4, **kwargs: Any) -> None: """Function used to log info-level messages only on global rank 0.""" _info(*args, stacklevel=stacklevel, **kwargs)
def _warn(message: Union[str, Warning], stacklevel: int = 2, **kwargs: Any) -> None: if type(stacklevel) is type and issubclass(stacklevel, Warning): rank_zero_deprecation( "Support for passing the warning category positionally is deprecated in v1.6 and will be removed in v1.8" f" Please, use `category={stacklevel.__name__}`." ) kwargs["category"] = stacklevel stacklevel = kwargs.pop("stacklevel", 2) warnings.warn(message, stacklevel=stacklevel, **kwargs)
[docs]@rank_zero_only def rank_zero_warn(message: Union[str, Warning], stacklevel: int = 4, **kwargs: Any) -> None: """Function used to log warn-level messages only on global rank 0.""" _warn(message, stacklevel=stacklevel, **kwargs)
[docs]class LightningDeprecationWarning(DeprecationWarning): """Deprecation warnings raised by PyTorch Lightning."""
rank_zero_deprecation = partial(rank_zero_warn, category=LightningDeprecationWarning)

© Copyright Copyright (c) 2018-2023, Lightning AI et al...

Built with Sphinx using a theme provided by Read the Docs.